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Multivariate hierarchical analysis of car crashes data considering a spatial network lattice
The Journal of the Royal Statistical Society, Series A (Statistics in Society) ( IF 2 ) Pub Date : 2022-03-27 , DOI: 10.1111/rssa.12823
Andrea Gilardi 1 , Jorge Mateu 2 , Riccardo Borgoni 3 , Robin Lovelace 4
Affiliation  

Road traffic casualties represent a hidden global epidemic, demanding evidence-based interventions. This paper demonstrates a network lattice approach for identifying road segments of particular concern, based on a case study of a major city (Leeds, UK), in which 5862 crashes of different severities were recorded over an 8-year period (2011–2018). We consider a family of Bayesian hierarchical models that include spatially structured and unstructured random effects to capture the dependencies between the severity levels. Results highlight roads that are more prone to collisions, relative to estimated traffic volumes, in the north-west and south of city centre. We analyse the modifiable areal unit problem (MAUP), proposing a novel procedure to investigate the presence of MAUP on a network lattice. We conclude that our methods enable a reliable estimation of road safety levels to help identify ‘hotspots’ on the road network and to inform effective local interventions.

中文翻译:

考虑空间网络格的车祸数据的多变量层次分析

道路交通伤亡代表了一种隐藏的全球流行病,需要基于证据的干预措施。本文基于一个主要城市(英国利兹)的案例研究,展示了一种用于识别特别关注的路段的网络点阵方法,其中在 8 年期间(2011-2018 年)记录了 5862 起不同严重程度的撞车事故. 我们考虑了一系列贝叶斯层次模型,其中包括空间结构化和非结构化随机效应,以捕获严重性级别之间的依赖关系。结果突出显示了市中心西北部和南部相对于估计交通量而言更容易发生碰撞的道路。我们分析了可修改的面积单元问题 (MAUP),提出了一种新的程序来调查网络格上 MAUP 的存在。
更新日期:2022-03-27
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